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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Computational Methods for Rational Drug Design
- •Contents
- •1.1.2.2 GROMACS
- •1.1.2.3 Amber
- •1.1.2.4 CHARMM
- •1.1.2.5 AutoDock
- •1.1.2.6 VMD
- •1.1.2.7 PyMOL
- •1.1.2.8 Open Babel
- •List of Contributors
- •Preface
- •1. Molecular Modeling and Drug Design
- •1.1 Introduction
- •1.1.1 What Is Molecular Modeling?
- •1.1.2 Software Used for Molecular Modeling
- •1.1.2.1 Schrodinger
- •1.1.2.9 Avogadro
- •1.1.2.10 Discovery Studio
- •1.1.3 Molecular Mechanics
- •1.1.3.1 Prediction of Binding Affinity
- •1.1.3.2 Conformational Analysis
- •1.1.3.3 Virtual Screening
- •1.1.3.4 Lead Discovery
- •1.1.3.5 Mechanism of Action
- •1.2 Types of Molecular Models
- •1.2.1 Ball-and-Spoke Model
- •1.2.1.1 Future Directions
- •1.2.2 Space-filling Models
- •1.2.2.1 Future Directions
- •1.2.3 Crystal Lattice Models
- •1.2.3.1 Future Directions
- •1.3 Computational Methods in Drug Discovery
- •1.3.1 What Is Drug Discovery?
- •1.3.2 Computational Platforms for Drug Discovery
- •1.3.2.1 NCBI
- •1.3.2.2 Chemical Databases
- •1.3.2.3 PDB
- •1.3.2.5 UniProt
- •1.3.2.6 QSAR
- •1.3.2.8 Desmond
- •1.3.2.9 OpenBabel
- •1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
- •1.3.2.11 SBML
- •1.3.2.12 Virtual Screening
- •1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
- •1.4 Potential Use and Application of AI in Drug Designing
- •1.4.1 Target Identification and Validation
- •1.4.2 Drug Screening and Lead Optimization
- •1.4.3 De Novo Drug Design
- •1.4.4 Predictive Toxicology and ADMET
- •1.4.5 Clinical Trial Optimization
- •1.4.6 Drug Repurposing
- •1.4.7 Concept of Personalized Medicine
- •1.4.8 Drug Combination Optimization
- •1.5 Limitations of Current Methods
- •1.5.1 Data Restrictions
- •1.5.2 Interpretability
- •1.5.3 Generalization
- •1.5.4 Resources and Computation
- •1.5.5 Ethical Considerations
- •1.5.6 Validation and Experimentation
- •1.5.7 Regulatory Obstacles
- •1.6 Case Studies
- •1.7 Molecular Docking
- •1.7.1 What Is Molecular Docking?
- •1.7.1.1 Procedure
- •1.7.1.2 Biophysical Laws
- •1.7.1.3 Rigid and Flexible Docking
- •1.7.1.4 Types of Docking
- •1.7.1.5 Challenges and Future Perspectives
- •1.7.2 Applications of Molecular Docking in Drug Designing
- •1.7.3 Success of Molecular Docking Cases in Drug Designing
- •1.8 Conclusion and Future Works
- •References
- •2. Bioactive Small Molecules and Drug Discovery
- •2.1 Introduction
- •2.1.1 Introduction to Drug Design and Discovery
- •2.1.2 Brief History of Small-Molecule Drug Discovery
- •2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
- •2.2.1 Structure-Based Methods
- •2.2.2 Ligand-Based Methods
- •2.2.3 Network-Based Methods
- •2.3 Natural Products in Bioactive Small-Molecule Discovery
- •2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
- •2.3.2 Anticancer Agents as Bioactive Molecules
- •2.3.3 Antiviral Agents as Bioactive Molecules
- •2.3.4 Antimalarial Agents as Bioactive Molecules
- •2.6.6 Toxicity and Side Effects
- •2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
- •2.6.8 Structural Diversity and Novelty
- •2.6.9 Patentability and Intellectual Property
- •2.3.5 Marine Bioactive Products
- •2.4.1 Importance of DFT in Small-Molecule Drug Discovery
- •2.5 Application of DFT to Bioactive Small Molecules
- •2.5.1 HOMO–LUMO Calculation
- •2.5.1.1 Molecular Electrostatic Potential (MEP) Map
- •2.5.1.3 Natural Bond Orbital (NBO) Analysis
- •2.5.1.4 Implementations and Tools
- •2.6.1 Target Identification and Validation
- •2.6.2 Target Specificity
- •2.6.3 Bioavailability and Pharmacokinetics
- •2.6.4 Chemical Structure and Drug-likeness
- •2.6.5 Safety and Toxicity
- •2.7 Conclusion
- •References
- •3. Novel Drug Targets for Small Molecule-based Drug Discovery
- •3.1 Introduction
- •3.2 Drug Target Identification
- •3.3 Classification of Novel Drug Targets
- •3.3.1 Transcription Factors
- •3.3.2 Cytokines
- •3.3.3 Chaperones
- •3.3.4 Viral Targets
- •3.3.5 G Protein-coupled Receptors
- •3.3.6 Transporters
- •3.3.7 Enzymes
- •3.3.8 RNA Targets
- •3.4 Small Molecules as Drugs
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Structure-Based Drug Discovery Concept
- •4.2.1 Structure Generation of the Target
- •4.2.1.1 The Detailed Description of Each Tool
- •4.2.2 Active Binding Site Within the Target
- •4.2.2.1 The Detailed Description of Each Tool
- •4.2.2.2 Molecular Docking Analysis
- •4.2.2.3 The Detailed Description of Each Tool
- •4.2.3 Molecular Dynamic Simulations
- •4.2.3.1 The Detailed Description of Each Tool
- •4.3 Ligand-Based Drug Discovery Concept
- •4.3.1.1 The Detailed Description of Each Tool
- •4.4 Structure- and Ligand-Based Assisted Studies
- •4.4.1 The Detailed Description of Each Tool
- •4.4.2 The Detailed Description of Each Tool
- •4.5 Advancement and Challenges in SBDD and LBDD
- •4.6 Conclusion
- •References
- •5. Virtual Screening and Lead Discovery
- •5.1 Introduction to Virtual Screening and Lead Discovery
- •5.1.1 Overview of Drug Discovery Process
- •5.1.2 Role of Virtual Screening
- •5.1.3 Importance of Lead Discovery
- •5.2 Molecular Targets and Biomolecular Structures
- •5.3 Virtual Screening Approaches
- •5.3.1 Structure-based Virtual Screening
- •5.3.2 Ligand-based Virtual Screening
- •5.3.3 Hybrid Approaches
- •5.4 Databases and Compound Collections
- •5.4.1 Overview of Chemical Databases
- •5.4.2 Compound Filtering and Preparation
- •5.4.3 Diversity and Size of Compound Collections
- •5.5 Molecular Docking
- •5.5.1 Principles of Molecular Docking
- •5.5.2 Docking Algorithms and Scoring Functions
- •5.5.3 Validation of Docking Results
- •5.6 Pharmacophore Modeling
- •5.6.1 Concept of Pharmacophores
- •5.6.2 Generating Pharmacophore Models
- •5.6.3 Applications in Lead Discovery
- •5.7 Quantitative Structure–Activity Relationship (QSAR)
- •5.7.1 Basics of QSAR
- •5.7.2 Model Development and Validation
- •5.7.3 QSAR in Virtual Screening
- •5.8 Machine Learning and AI in Virtual Screening
- •5.8.1 Introduction to Machine Learning and AI
- •5.8.2 Feature Selection and Model Training
- •5.8.3 Applications in Virtual Screening
- •5.9 Hit-to-Lead Optimization
- •5.9.1 Prioritizing Hits from Virtual Screening
- •5.9.2 SAR Analysis and Iterative Design
- •5.9.2.1 SAR Analysis (Structure–Activity Relationship)
- •5.9.2.2 Iterative Design
- •5.9.3 ADME/Tox Considerations
- •5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
- •5.9.3.2 Toxicity Considerations
- •5.10 Case Studies and Examples
- •5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
- •5.11 Challenges and Future Directions
- •5.11.1 Limitations of Virtual Screening
- •5.11.2 Emerging Technologies and Trends
- •5.11.3 Integration with High-throughput Experimentation
- •5.12 Ethical and Regulatory Considerations
- •5.12.1 Intellectual Property and Patents
- •5.12.2 Ethical Use of Computational Tools
- •5.12.3 Regulatory Approval Process
- •5.13 Conclusion
- •5.13.1 Future Prospects in Virtual Screening and Lead Discovery
- •5.13.2 Summary of Key Points
- •References
- •6. ADMET and Physicochemical Assessments in Drug Design
- •6.1 ADMET
- •6.1.1 Absorption
- •6.1.1.1 Solubility and Dissolution
- •6.1.1.2 Lipophilicity
- •6.1.1.3 Permeability
- •6.1.2 Distribution
- •6.1.3 Metabolism
- •6.1.4 Excretion
- •6.1.5 Toxicity
- •6.2 Physicochemical Assessments
- •6.2.1 Partition Coefficient
- •6.2.2 Log D: Ionizable Compound Lipophilicity
- •6.2.2.1 Methods for Calculating Lipophilicity
- •6.2.2.2 Direct Experimental Determination of Lipophilicity
- •6.2.2.3 Indirect Experimental Determination of Lipophilicity
- •6.2.3 Acid–Base Properties and Ionization
- •6.2.4 Solubility
- •6.2.5 Polymorphism
- •6.2.6 Molecular Weight
- •6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA)
- •References
- •7. In Silico Modeling and Drug Design
- •7.1 Introduction
- •7.2 Target Identification
- •7.2.1 Experimental Approaches
- •7.2.2 Computational Target Identification
- •7.2.3 Target Validation
- •7.3 Computer-Aided Drug Design
- •7.3.1 Ligand-based CADD
- •7.3.2 Structure-Based CADD
- •7.4 ADMET Assessment
- •7.5 Conclusion
- •References
- •8. Pharmacophore Modeling in Drug Design
- •8.1 Introduction
- •8.1.1 The Role of Pharmacophore Modeling in Drug Design
- •8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
- •8.2 Essential Concepts in Pharmacophore Hypothesis Generation
- •8.2.1.1 Partitioning Initial Data into Distinctive Datasets
- •8.3 Diverse Approaches to Pharmacophore Modeling
- •8.3.1 Ligand-Based Pharmacophore Modeling
- •8.3.2 Structure-Based Pharmacophore Modeling
- •8.4 Application of Pharmacophore Modeling
- •8.4.1 Applications of Pharmacophore-Based Virtual Screening
- •8.4.1.1 Drug Discovery
- •8.4.2 Applications in Drug Target Fishing
- •8.4.3 Applications in Ligand Profiling
- •8.4.4 Applications in Docking
- •8.4.5 Applications in ADMET
- •8.4.6 Modulation of the Immune System
- •8.5 Emerging Trends in Pharmacophore Model Development
- •8.5.1 Involvement of Machine Learning
- •8.5.2 Prediction of Pharmacokinetic Properties
- •8.5.3 Structural Biology and Protein Functionality Studies
- •8.5.4 Integration with MDs Simulations
- •8.6 Case Studies
- •8.6.1 Case 1
- •8.6.2 Case 2
- •8.7 Challenges in Pharmacophore Modeling
- •8.8 Conclusion
- •Acknowledgments
- •References
- •9. Scaffold Hopping and De Novo Drug Design
- •9.1 Introduction
- •9.2 Scaffold Hopping
- •9.2.1 Classification of Scaffold Hopping
- •9.2.1.1 1° Hop: Heterocycle Replacement
- •9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
- •9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
- •9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
- •9.2.2 Advantages of Scaffold Hopping
- •9.2.3 Disadvantages of Scaffold Hopping
- •9.2.4 Reasons for Scaffold Hopping
- •9.2.5 Properties and Key Methods of Scaffold Hopping
- •9.3 De Novo Drug Design
- •9.3.1 Classification of De Novo Drug Design
- •9.3.1.1 Structure-based Drug Design
- •9.3.1.2 Ligand-based Drug Design
- •9.3.1.3 De Novo Design Strategies
- •9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
- •9.3.1.5 Hybrid Approaches
- •9.3.2 Basic Principle of De Novo Drug Design
- •9.3.3 Application of De Novo Drug Design
- •9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
- •9.3.5 Methodological Approaches in De Novo Drug Design
- •9.3.5.1 Structure-based De Novo Drug Design
- •9.3.5.2 Ligand-based De Novo Drug Design
- •9.3.5.3 Generation of Drug-Like Molecular Fragments
- •9.3.5.4 Similarity Searching
- •9.3.5.5 Selection of Target Reference Structure
- •9.3.5.6 Similarity Analysis of De Novo-generated Compounds
- •9.3.5.7 Evaluation of Scaffold Diversity
- •9.4 Results and Discussion
- •9.4.1 Generation of Drug-Like Molecular Fragments
- •9.4.2 De Novo Design with a Single Reference Structure
- •9.4.3 De Novo Design with a Focused Set of Five Similar Templates
- •9.4.4 De Novo Design with a Diverse Set of Five Templates
- •9.6 Case Study
- •9.6.1 De Novo Drug Design
- •9.6.2 Scaffold Hopping
- •9.7 Conclusion
- •References
- •10. Fragment-based Drug Design and Drug Discovery
- •10.1 Introduction
- •10.2 The Process of Finding Fragments
- •10.3 FBDD Strategies
- •10.4 Case Studies
- •10.5 Conclusion and Future Perspectives
- •References
- •11. AI/ML Approaches in Drug Design
- •11.1 Introduction
- •11.2 Traditional Drug Design Methods
- •11.2.1 The Rise of Computational Methods
- •11.2.2 The Importance of AI/ML in Modern Drug Design
- •11.3 AI/ML Landscape in Drug Design
- •11.3.1 AI/ML Algorithms and Methods
- •11.3.1.1 Machine Learning Models
- •11.3.1.2 Neural Networks
- •11.3.2 Applications in Drug Design
- •11.3.2.1 Peptide Synthesis
- •11.3.2.2 Molecular Design
- •11.3.2.3 Virtual Screening (VS)
- •11.3.2.4 Quantitative Structure–Activity Relationship Models
- •11.3.2.5 Drug Repurposing
- •11.3.3 Challenges and Failures
- •11.4 Ethics, Reliability, and Regulatory Issues
- •11.5 Future Directions
- •11.6 Conclusion
- •References
- •12. Network-based Methods in Drug Discovery
- •12.1 Introduction
- •12.1.1 Background of Drug Discovery Future Challenges
- •12.1.2 Single Target Approach Limitations
- •12.1.3 Emergence of Network Biology and Polypharmacology
- •12.2 Network Pharmacology: Practical Guide
- •12.2.1 Common Network Pharmacology Databases
- •12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
- •12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
- •12.2.1.3 Target Genes of Phytoconstituents
- •12.2.2 Network Analysis and Visualization
- •12.2.3 Applications of Network Pharmacology in Drug Discovery
- •12.3 Ayurveda and Traditional Indian Medicine
- •12.3.1 Overview of Ayurveda and Its Complex Formulations
- •12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
- •12.4 Network Pharmacology in Herbal Remedies
- •12.4.1 Application of Network Pharmacology in Herbal Drug Discovery
- •12.4.1.1 Cancer
- •12.4.1.2 Cardiovascular Diseases (CVDs)
- •12.4.1.3 Diabetes Mellitus (DM)
- •12.4.2 Screening Pharmacological Efficacy of Herbal Remedies
- •12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases
- •12.5 Conclusion and Future Prospects
- •References
- •13. Rational Design of Natural Products for Drug Discovery
- •13.1 Introduction
- •13.2 Natural Products for the Development of New Drugs
- •13.3 Criteria for Selecting Natural Products for Drug Design
- •13.4 Importance of Biodiversity in Sourcing Natural Products
- •13.5 Structural Elucidation of Natural Products
- •13.6.3 High-Throughput Screening Methods for Efficient Compound Selection
- •13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability
- •13.6.5 ADMET Attributes Predicted In Silico
- •13.7 Formulation Challenges with Natural Products
- •13.8 Quality by Design (QbD) Approaches
- •13.8.1 Use of Computational Models for Formulation Optimization
- •13.9 Conclusion
- •References
- •14. Design of Enzyme Inhibitors in Drug Discovery
- •14.1 Introduction
- •14.3 Classification of Enzyme Inhibitors
- •14.3.1 Reversible Inhibitors
- •14.3.2 Irreversible Inhibitors
- •14.3.3 Competitive Inhibitors
- •14.3.4 Noncompetitive Inhibitors
- •14.3.5 Allosteric Modulators
- •14.4.1 Structure-Based Design
- •14.4.2 Computer-Aided Design
- •14.4.3 Fragment-Based Design
- •14.4.4 Virtual Screening Method
- •14.4.4.1 Ligand Based
- •14.4.4.2 Receptor Based
- •14.4.5 Natural Product-Based Discovery
- •14.4.6 Using Iterative Protein Crystallographic Analysis
- •14.4.7 Utilization of Covalent Inhibitors
- •14.4.8 Encapsulation Techniques
- •14.4.9 Based on Active-Site Specificity
- •14.4.10 Machine Learning Inhibitor Design
- •14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry
- •14.5 Limitations and Challenges
- •14.6 Future Directions
- •14.7 Conclusion
- •References
- •15.1 Introduction
- •15.2 Peptides as Therapeutics
- •15.2.1 Peptide Antibiotics
- •15.2.1.1 Peptides in Bone Diseases
- •15.2.1.2 Peptides in Cancer
- •15.2.1.3 Peptides in Metabolic Diseases
- •15.2.1.4 Peptides in Gastrointestinal Diseases
- •15.2.2 Advantages and Limitations of Peptide Therapeutics
- •15.2.3 FDA-Approved Peptide Therapeutics
- •15.2.4 Peptide-Based Entities in Clinical Trials
- •15.2.5 Peptide Synthesis and Diversification
- •15.2.5.1 Chemical Synthesis of Peptides
- •15.2.5.2 Chemical Modification of Peptide and Peptidomimetics
- •15.2.5.3 Backbone Modification of Peptides
- •15.2.5.4 Side-Chain Modification of Peptides
- •15.2.5.5 Peptide Cyclization
- •15.2.5.6 Peptide Mimicking of α-Helices and Stabilization
- •15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets
- •15.2.5.8 Peptide Production by Recombinant Technology
- •15.2.5.9 Peptides Modification by Genetic Code Expansion
- •15.2.5.10 PEGylation of Peptides and Proteins
- •15.3 New Technologies for Peptide-Based Drug Discovery
- •15.3.1 Phage Display
- •15.3.2 mRNA Display
- •15.3.3 DNA-Encoded Libraries
- •15.3.4 Cell-Penetrating Peptides
- •15.3.5 Macrocyclic Peptides
- •15.4 Computational Approaches in Peptide Drug Discovery
- •15.5 Conclusion
- •References
- •16. Rational Design of Drugs for Neurodegenerative Disorders
- •16.1 Introduction
- •16.2 Common Mechanism of Neurodegeneration
- •16.3 Brief Overview of Computational Methods in Drug Design
- •16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder
- •16.4.1 Epidemiology of Parkinson’s Disease
- •16.4.2 Pathogenesis of PD
- •1) Accumulation of Lewy bodies in substantia nigra
- •2) Mitochondrial dysfunction
- •3) Genetic factors
- •4) Neuroinflammation
- •5) Impaired protein handling
- •6) Oxidative stress
- •7) Environmental toxins
- •16.4.3 Signaling Pathway of Parkinson’s Disease
- •1) DA signaling
- •2) MAPK/ERK pathway
- •3) PI3K/Akt/mTOR pathway
- •4) Wnt/β-catenin pathway
- •5) NF-κB (nuclear factor-κB) pathway
- •6) Autophagy-lysosomal pathway
- •7) JNK (c-Jun N-terminal kinase) pathway
- •8) AMPK (AMP-activated protein kinase) pathway
- •9) Nrf2 (nuclear factor erythroid 2-related factor 2) pathway
- •16.4.4 Enzymatic Targets in Parkinson’s Disease
- •1) MAO-B (monoamine oxidase B)
- •2) COMT (catechol-O-methyltransferase)
- •3) LRRK2
- •4) GCase (glucocerebrosidase)
- •5) PARP-1 [poly(ADP-ribose) polymerase-1]
- •6) PINK1
- •7) DJ-1 (Parkinson protein 7)
- •8) Nrf2
- •16.4.5 Current Therapeutic Approaches to Treat PD
- •1) Drugs to treat motor symptoms of PD
- •2) Drugs to treat non-motor symptoms of PD
- •3) Disease-modifying therapies to treat PD
- •16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
- •1) Symptomatic relief only
- •2) Motor fluctuations and dyskinesias
- •3) Limited efficacy in nonmotor symptoms
- •4) Disease progression
- •5) Side effects
- •6) Limited treatment options for advanced PD
- •7) Individual variability
- •16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
- •16.4.8 Significance of Computational Approaches in Parkinson’s Disease
- •16.4.9 Use of Computational Tools in Identifying Biomarkers
- •16.4.10 Neuroprotective Strategies Through Computational Insights
- •16.4.10.1 Computational Models for Neuroprotection
- •1) Target identification and validation
- •2) Drug repurposing
- •3) Alpha-synuclein aggregation inhibitors
- •4) Deep learning in biomarker discovery
- •5) Personalized medicine
- •6) Drug-induced neuroprotection
- •7) Optimizing clinical trials
- •1) ML and AI-based diagnostics
- •2) Wearable technology integration
- •3) Multimodal data fusion
- •4) Predictive modeling of disease progression
- •5) Network analysis of brain connectivity
- •6) Personalized treatment optimization
- •7) Data sharing and collaboration platforms
- •16.5 Conclusion
- •References
- •17. Rational Design of Anti-inflammatory Therapeutics
- •17.1 Introduction
- •17.2 Navigating Inflammation and its Microenvironment
- •17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
- •17.2.2 Acidosis
- •17.2.3 Increased Oxidative Stress in Tissues
- •17.3 The Demand for Advanced Anti-inflammatory Medications
- •17.5 Rational Design of Anti-inflammatory Agents
- •17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids
- •17.5.3 Rational Design of Novel Aminopiperidinyl Amide
- •17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents
- •17.6 Conclusion and Future Perspectives
- •Authors’ Contribution
- •References
- •18.1 Introduction
- •18.2 Treatment
- •18.3 Antibacterial Resistance
- •18.3.1 Mutation
- •18.3.2 Horizontal Gene Transfer (HGT)
- •18.3.3 Enzymatic Modification or Degradation
- •18.3.4 Target Site Modification
- •18.3.5 Decreased Permeability
- •18.3.6 Efflux Pumps
- •18.3.7 Plasmids
- •18.3.8 Transposons
- •18.3.9 Gene Amplification
- •18.3.10 Formation of Biofilms
- •18.3.11 Modified Metabolic Pathways
- •18.3.12 Adaptive Evolution
- •18.4.1 Structure- Based Drug Design
- •18.4.2 Modification of Existing Antibiotics
- •18.4.3 Bioisosterism
- •18.4.4 Prodrug Strategies
- •18.4.5 Similar Bacterial Components Target
- •18.4.6 Combine or Combination Therapy
- •18.4.7 Drug Repurposing
- •18.4.8 Resistant Mechanism Blocking
- •18.4.9 Improving Drug Delivery by Nanotechnology
- •18.4.10 Phage Intervention
- •18.4.11 Host Targeting
- •18.4.12 CRISPR-Cas Technique
- •18.4.13 Peptides as Antibacterials
- •18.4.14 Immunizations and Immunotherapy
- •18.4.15 Natural Product Derivatives
- •18.4.16 Fragment- Based Drug Discovery (FBDD)
- •18.4.17 Metabolomics and Genetics
- •18.4.18 Cheminformatics
- •18.5 Summary and Conclusion
- •References
- •19. Rational Design of Antiviral Therapeutics
- •19.1 Introduction to Antiviral Therapeutics
- •19.1.1 Overview
- •19.1.2 Blueprints for Antiviral Drug Interventions
- •19.1.2.1 Protein Folding and Binding Sites
- •19.1.2.2 Conformational Changes
- •19.1.2.3 Protein–Protein Interactions (PPIs)
- •19.1.2.4 Capsid and Envelope Structures
- •19.1.2.5 Structural Vulnerabilities
- •19.1.2.6 Enzymatic Activities
- •19.1.2.7 Viral Attachment
- •19.1.2.8 Viral Assembly and Replication Machinery
- •19.1.2.9 The Host’s Immune Response
- •19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
- •19.2.1 Enzyme Inhibitors
- •19.2.2 Antiviral Peptides
- •19.2.3 Antiviral Antibodies
- •19.2.4 Lipid-Mimicking Compounds
- •19.2.5 Vaccines
- •19.2.6 Immunomodulation
- •19.3 Rational Strategies for Antiviral Therapeutics
- •19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
- •19.3.2 AI and ML
- •19.3.3 Systems Biology and Network Pharmacology
- •19.3.4 CRISPR Systems
- •19.3.5 Nanotechnology-Based Design and Delivery Systems
- •19.3.6 Reverse Vaccinology
- •19.4 Conclusion
- •References
- •20. Rational Design of Anticancer Therapeutics
- •20.1 Introduction
- •20.2 Rational Design of Nanomedicine for Cancer Treatment
- •20.4.1 Particle Size
- •20.4.2 Shape
- •20.4.3 Surface Modification
- •20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
- •20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
- •20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
- •20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
- •20.7 Conclusion
- •References
- •21. PROTAC and ProTide Strategies in Drug Design
- •21.1 Introduction
- •21.2 Drug Design: Past to Present
- •21.3 PROTAC Strategy in Drug Design
- •21.3.1 Ubiquitin Proteasome System and PROTACs
- •21.3.2 Chemical Formulations of PROTACs
- •21.3.3 Advent of PROTACs as Antiviral
- •21.3.4 NS3/4A-Targeting PROTACs Against HCV
- •21.3.4.1 Neuraminidase-Targeting PROTACs
- •21.4 Emergence of ProTide Technology in Drug Design
- •21.5 Approaches of ProTides in Drug Development
- •21.6 Implementation of ProTides as Nucleoside Analogs
- •21.6.1 Antiviral Applications of ProTides
- •21.7 Conclusion
- •References

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13.1 Introduction
In the relentless pursuit of novel therapeutic solutions, pharmaceutical research continually
explores the vast array of natural compounds. These compounds, found abundantly in nature,
offer a treasure trove of chemical diversity and biological activity, making them promising candi-
dates for drug development [1]. However, harnessing the full potential of natural products for
medicinal purposes presents numerous challenges, including intricate chemical structures, lim-
ited bioavailability, and potential side effects. To overcome these hurdles and expedite the discov-
ery of effective drugs derived from natural sources, scientists have turned to rational drug
design – an approach rooted in molecular biology, biochemistry, and computational chemistry [2].
This chapter delves into the intricacies of rational drug design, exploring its methodologies, appli-
cations, and implications for advancing drug discovery.
Structure-guided computer-aided drug design is a cornerstone of rational drug design, particu-
larly for targets with known three-dimensional structures. By elucidating the binding interactions
between target proteins and potential drug candidates, this approach enables researchers to design
small molecules with enhanced specificity and efficacy [3]. Utilizing techniques such as X-ray
crystallography and nuclear magnetic resonance (NMR) spectroscopy, scientists can visualize the
three-dimensional structure of target proteins and identify key binding sites [4]. Virtual screening
(VS) of large chemical libraries further expedites the identification of promising drug candidates,
laying the foundation for subsequent experimental validation and optimization.
In the era of precision medicine, state-of-the-art methodologies play a pivotal role in driving
drug discovery forward. Resources such as the Protein Data Bank (PDB), the National Cancer
Institute (NCI) database, and computational tools provide researchers with invaluable resources
for VS and structural analysis. High-throughput screening (HTS) platforms and molecular mode-
ling software further streamline the drug discovery process, enabling rapid iteration and optimiza-
tion of lead compounds [5].
While modern drug discovery relies heavily on cutting-edge technologies, traditional medicinal
practices offer valuable insights into the therapeutic potential of natural compounds. Ancient
13
Rational Design of Natural Products for Drug Discovery
Ankita Kashyap
1
, Anupam Sarma
2
, Bhrigu Kumar Das
3
, and Ashis Kumar Goswami
4
1
Institute of Pharmacy, Assam Medical College and Hospital, Dibrugarh, India
2
Advanced Drug Delivery Laboratory, School of Pharmaceutical Sciences, Girijananda Chowdhury University, Guwahati, India
3
Pharmacology and Toxicology Laboratory, School of Pharmaceutical Sciences, Girijananda Chowdhury University, Guwahati, India
4
Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Assam, India

286
Asian herbal medicine, such as Ayurveda, emphasizes the synergistic effects of herbal combina-
tions, paving the way for novel approaches to drug discovery. By integrating traditional wisdom
with modern scientific techniques, researchers can unlock new therapeutic avenues and address
unmet medical needs more effectively [6].
Global gene expression profiling provides unprecedented insights into disease pathogenesis and
therapeutic responses. Platforms such as Serial Analysis of Gene Expression (SAGE) and microar-
ray analysis enable researchers to characterize the transcriptional profiles of diseased tissues and
identify dysregulated genes associated with disease progression. Bioinformatics tools facilitate the
analysis and interpretation of vast genomic datasets, guiding the selection, and optimization of
drug candidates with the highest therapeutic potential [7].
In conclusion, rational drug design represents a paradigm shift in drug discovery, integrating
cutting-edge technologies with traditional wisdom to unlock nature’s pharmacological arsenal. By
harnessing the power of computational modeling, structural biology, and genomic analysis,
researchers can expedite the discovery and development of safe and effective therapeutics. As we
continue to unravel the complexities of disease biology and molecular interactions, rational drug
design will remain at the forefront of drug discovery, shaping the future of medicine and improv-
ing patient outcomes.
13.2 Natural Products for the Development of New Drugs
Natural products constitute a diverse array of chemical compounds biosynthesized by living organ-
isms, encompassing plants, animals, and microorganisms. Endowed with a rich history of applica-
tion as curative agents dating back millennia, these compounds hold profound significance due to
their inherent bioactivity and promising therapeutic attributes. Alkaloids, flavonoids, terpenes,
and steroids are among the myriad subclasses of natural products, each contributing distinct
molecular structures and pharmacological potential. Their pivotal role in contemporary drug dis-
covery underscores their value as sources of novel therapeutic agents. Natural products, with their
complex and intricate chemical profiles, continue to captivate scientific interest as they unveil a
treasure trove of potential therapeutic preparations, perpetuating their legacy as indispensable
contributors to the advancement of medicine [8]. Figure 13.1 shows the components or fundamen-
tal steps involved in developing new drugs from natural products.
Natural products for the development of new drugs
Identification of
potential sources
Post-marketing
surveillance
Lead optimization
Bioactivity
screening
Isolation and
characterization
Mechanism of
action studies
Preclinical studies
Clinical
development
ADME/toxicity
assessment
Patenting and
intellectual property
Figure 13.1 Components of the process of development of new drugs from natural products.

287
They are essential to the drug development process because of their unique chemical originality
and deep biological activity. These substances have played a significant role in the pharmaceutical
industry throughout history, accounting for around 30% of all drug sales worldwide. Natural prod-
ucts are unmatched resources because they have a vast reservoir of chemical variety, attractive
drug-like characteristics, and the ability to interact with various biological target proteins. Their
historical role as foundational lead compounds in drug discovery is augmented by an escalating
recognition of their intricate structural and stereochemical attributes. In contemporary drug devel-
opment initiatives, the enduring relevance of natural products persists, positioning them as invalu-
able reservoirs for the innovation and creation of novel therapeutic drugs [9, 10].
Sales of recombinant peptides and protein molecules are on the rise, but low-molecular-mass
chemicals continue to be the gold standard for treating human diseases. This is mainly because of
their greater compliance and bioavailability. Natural products have historically been the source of
many novel therapeutic methods, demonstrating their creative potential in medication discovery
and development. Natural products have had a significant impact on drug development, but pres-
ently, they are not given as much attention because of the significant work involved in active prin-
ciple isolation and structure elucidation. To effectively compete with combinatorial chemistry,
new approaches in natural product chemistry are needed to meet the requirement for large test
samples in the process of HTS. Pharmaceutical companies currently spend about $350 million US
on the development of new drugs. It is predicted that the course of medication development and
discovery in the future will depend on advances in molecular science, the biology of cells, and
genome engineering to improve therapeutic target clarification and lead discovery. The human
genome is almost complete, which means that a much larger pool of possible pharmacological
targets will become available for consideration during the drug discovery phase. The ability of HTS
to test an increasing number of samples highlights the significance of creating large chemical col-
lections with improved structural diversity for more successful drug development programs [11].
The landscape of natural product drug discovery confronts formidable challenges arising from
the extensive chemical diversity inherent in these compounds, coupled with technical barriers. A
comprehensive survey involving 52 experts underscored a perceptible dissonance between the
acknowledged potential of natural products and the efficacy of prevailing drug discovery strategies.
Paramount among the challenges are the constrained supply of compounds, hurdles in target
identification, and the complexities associated with scale-up for commercialization. In response to
these impediments, a strategic amalgamation of rational approaches, cutting-edge technologies,
and computational methodologies is being orchestrated [12]. This integration encompasses
structure- and ligand-based design techniques, ADMET (absorption, distribution, metabolism,
excretion, and toxicity) profiling, and the utilization of cheminformatics for the systematic
organization and interpretation of data. Despite the intricacies posed by these challenges, the
intrinsic value of natural products in drug discovery endures as ongoing efforts strive to harness
their potential through innovative methodologies and technologies [13].
Contemporary research in medicinal plant-based drug discovery adopts a multifaceted approach,
integrating fractionation guided by bioassay, combinatorial chemistry, botany, phytochemistry,
and biochemistry methodologies. In the quest for safe and effective drugs, natural sources con-
tinue to be explored as valuable reservoirs of pharmacological leads against severe diseases like
diabetes, cardiovascular disorders, and cancer. The escalating global demand for reliable therapeu-
tics has prompted a resurgence in exploring natural resources for potential treatments of chronic
and life-threatening illnesses. However, the sector faces significant obstacles, particularly in the
marketing and scaling up of active compounds, meaning that only a small percentage of lead mol-
ecules may advance to the stage of medication development. For the effective investigation and

288
development of medicinal compounds derived from natural resources, it is essential to take a
methodical and scientific approach to overcome these challenges [14].
The foundational role of conventional techniques persists as paramount in the identification of
potential therapeutic agents. Historically, natural products have stood as invaluable reservoirs of drug
leads, with bio-guided fractionation being a conventional approach wherein crude extracts undergo
sequential separation and testing until one single active ingredient is found. However, this approach
is characterized by a laborious and slow procedure. In response to these limitations, contemporary
strategies, such as metabolomics and chemometrics, have emerged to enhance lead-finding efficiency
by establishing correlations between chemical and biological data. Despite the inherent challenges,
natural products maintain their pivotal status as a crucial source for the discovery of novel drugs,
evolving alongside innovative methodologies aimed at optimizing the drug discovery process [15].
13.3 Criteria for Selecting Natural Products for Drug Design
The selection of natural products for drug design involves carefully considering several criteria to
identify promising candidates with therapeutic potential. By carefully evaluating natural products
based on these criteria, researchers can identify promising drug design and development candidates,
ultimately discovering novel therapeutics with improved efficacy and safety profiles. These include:
1) Bioactivity: Natural products should exhibit specific biological activities relevant to the targeted
disease or condition, such as antimicrobial, anti-inflammatory, or anticancer properties. Screening
assays and bioassays are commonly used to assess the bioactivity of natural products [16].
2) Pharmacological properties: Natural products should possess favorable pharmacokinetic
and pharmacodynamic properties, including ADMET. Compounds with suitable ADMET pro-
files are more likely to be developed into successful drugs [17, 18].
3) Chemical diversity: Natural products offer various chemical structures, representing diverse
scaffolds and functional groups. Selecting compounds with structural diversity increases the like-
lihood of identifying novel drug leads and optimizing their pharmacological properties [19, 20].
4) Synthetic accessibility: The feasibility of synthesizing or obtaining natural products in suffi-
cient quantities for further research and development is essential. Compounds that are readily
accessible or amenable to chemical synthesis are preferred for drug design efforts [21, 22].
5) Availability and sustainability: Natural products should be obtainable from sustainable and
environmentally friendly sources to ensure long-term availability. Ethical considerations
regarding biodiversity conservation and traditional knowledge protection should also be
considered [23].
6) Target specificity: Natural products should demonstrate selectivity toward the intended
molecular targets associated with the diseases of interest. Understanding the mechanism of
action and target engagement is crucial for assessing the therapeutic potential of natural
products [24].
13.4 Importance of Biodiversity in Sourcing Natural Products
The diversity and variability of life on Earth is known as biodiversity, and it is commonly measured by
differences found in species, genetic makeup, and ecosystems [25]. Natural products (NPs) are the
end-products of gene expression, also known as “secondary metabolites.” They are a reliable and vital

289
source of effective medication leads from Earth’s biodiverse flora and fauna [26]. Since ancient times,
the development of pharmaceutical products has been aided by using natural products for medicinal
purposes. Drug research and discovery depend on biodiversity conservation since its loss eliminates
previously unknown genetic and natural chemical resources. The most well-known pharmacological
drugs from natural sources are caffeine, acetylsalicylic acid (Aspirin), and plants’ antimalarial agents
Quinine and Artemisinin [25]. Similarly, the majority of known antibiotics (such as Streptomycin and
Penicillin) and antifungal drugs (such as Griseofulvin) have been discovered through soil fungi [27].
Biodiversity, encompassing the vast array of life forms and ecosystems on Earth, is the corner-
stone of natural product sourcing for drug discovery. It represents the collective genetic, species,
and ecological diversity in different environments, ranging from tropical rainforests to marine eco-
systems. Within this biological diversity lie invaluable reservoirs of secondary metabolites, known
as natural products, which organisms have evolutionarily honed for various ecological purposes.
These natural products exhibit a remarkable array of chemical structures and bioactivities, making
them promising candidates for drug development [28]. The importance of biodiversity in sourcing
natural products for drug discovery cannot be overstated. It provides researchers with an extensive
library of chemical compounds with diverse pharmacological properties, offering potential solu-
tions to various human health conditions. The loss of biodiversity poses a significant threat to drug
discovery efforts, as it not only diminishes the availability of natural compounds but also erases the
genetic and chemical diversity that could hold the key to novel therapeutics. As such, conservation
efforts to preserve biodiversity are crucial to safeguarding these invaluable resources for future
drug development endeavors [29].
Historically, natural products have played a pivotal role in developing pharmacological agents,
serving as the foundation for many essential medications. Examples abound, from discovering
antibiotics like penicillin to developing anticancer drugs such as vincristine and vinblastine derived
from the Madagascar periwinkle. Moreover, recent advancements in drug discovery have contin-
ued to leverage biodiversity, with discoveries ranging from novel antidiabetic agents sourced from
plants to marine-derived compounds with potential antiparasitic and anticancer properties [30].
Piptoporus betulinus, another type of fungus that grows on birches, was cooked to create charcoal,
which has antibacterial and disinfecting properties, and different therapeutic cures have even been
supplied by marine organisms. A drink made from the red algae Chondrus crispus and Mastocarpus
stellatus was used to treat colds, sore throats, and chest illnesses, including tuberculosis. It was also
reported that boiling the algae in milk or water made them effective against burns and kidney
problems [31]. Also, compounds like Zamamidine D isolated from Amphimedon sp., Protulactone
A isolated from Aspergillus sp., Gombasterols from Clathria gombawuiensis, Lamellodysidines
from Lamellodysidea herbacea, Sulawesins from Psammocinia sp. have different medicinal proper-
ties [32]. In essence, preserving biodiversity is paramount for sustaining the reservoirs of natural
products that hold immense promise for future drug discovery efforts. By conserving Earth’s bio-
logical diversity, we can ensure continued access to the vast array of chemical compounds that
nature offers, thereby advancing the frontiers of pharmaceutical research and improving global
health outcomes [30].
13.5 Structural Elucidation of Natural Products
Over the past century, significant advancements have been made in elucidating the structures of
natural products, leading to a profound understanding of their chemical composition and biologi-
cal activity. This progress has been driven by innovative analytical techniques, methodologies, and

290
advancements in computational chemistry and spectroscopic instrumentation [18]. Historically,
the elucidation of natural product structures relied heavily on classical methods such as chemical
degradation, elemental analysis, and optical rotation measurements. However, the advent of NMR
spectroscopy in the mid-20th century revolutionized the field by providing detailed information
about the connectivity of atoms within molecules. NMR spectroscopy enabled researchers to elu-
cidate complex structures precisely and determine stereochemistry [33]. In parallel, mass spec-
trometry (MS) emerged as a powerful tool for analyzing natural products, offering insights into
molecular weight, fragmentation patterns, and elemental composition. The development of high-
resolution MS instruments further enhanced the accuracy and sensitivity of structural elucidation
studies, enabling the characterization of compounds present in minute quantities [34]. The syn-
ergy between NMR spectroscopy and MS has facilitated the elucidation of challenging natural
product structures, including complex alkaloids, terpenoids, and polyketides. Moreover, X-ray
crystallography has played a pivotal role in confirming the three-dimensional arrangement of
atoms in crystalline compounds, providing invaluable insights into molecular architecture and
stereochemistry [35]. In recent years, advancements in computational chemistry and bioinformat-
ics have complemented experimental approaches, enabling the prediction and modeling of natural
product structures based on spectroscopic data and molecular modeling techniques. These compu-
tational tools have expedited the structural elucidation process and facilitated the discovery of
novel bioactive compounds with therapeutic potential. Overall, the past century has witnessed
remarkable progress in elucidating natural product structures, driven by interdisciplinary collabo-
rations and technological innovations. Continued advancements in analytical techniques and
computational methods are expected to further research in natural product chemistry, unlocking
new avenues for drug discovery and development [36].
13.6 In Silico Computational Tools for Rational Drug Discovery
from Natural Sources
13.6.1 Molecular Docking and Virtual Screening Techniques for Predicting
Ligand–Receptor Interactions
Molecular docking stands as a computational cornerstone in drug discovery, employed to forecast
the interaction dynamics between a small molecule (ligand) and a target protein (receptor). Within
the realm of natural product drug discovery, this method assumes a pivotal role in scrutinizing
potential bioactive compounds derived from diverse sources, including plants, fungi, and marine
species. The primary objective is the discernment of natural products capable of interacting with
specific target proteins implicated in various diseases, ranging from cancer to neurodegenerative
disorders. By facilitating the screening and identification of promising lead compounds, molecular
docking becomes an instrumental tool, propelling the subsequent development of these com-
pounds into therapeutic agents. This computational approach thus serves as a strategic bridge
connecting the realms of computational science and pharmaceutical innovation in the pursuit of
novel and effective treatments [37].
Molecular docking emerges as an invaluable asset, facilitating the discernment of potential bio-
active molecules sourced from nature and thus catalyzing the quest for innovative therapeutic
agents. This method has demonstrated efficacy in pinpointing macromolecular targets for natural
products, thereby contributing to the development of drugs with heightened safety and efficacy
profiles [38]. Molecular docking’s utility extends to the expedited identification of bioactive

291
13.6 In Silico
phytochemicals, streamlining the preliminary validation process. Its versatility is evident in
medication design based on structure, exemplified by its application in the search for potential
inhibitors against COVID-19 [39, 40]. In essence, molecular docking assumes a pivotal role in
unlocking the therapeutic potential inherent in natural products, navigating the complex land-
scape of drug discovery with precision and efficiency [41].
Many natural compounds have large, pliable macrocyclic structures that interact with their tar-
gets using several sites of contact. Natural products are attractive scaffolds because of their unique
combination of limited elasticity and large surface area, especially for difficult therapeutic targets
and protein–protein interaction inhibition. Their preference for binding at target surfaces as
opposed to deep pockets highlights why medicinal chemistry campaigns find them appealing.
Their role as final therapeutic agents is no longer limited by the increasing ability to change these
chemicals through semisynthetic modifications or biosynthetic processes, which turns them into
suitable starting points for medicinal chemistry attempts. The logical improvement of molecular
docking in modern medicinal chemistry, guided by knowledge of the complex binding mecha-
nisms and adaptable scaffolds present in macrocyclic natural products and their analogs, greatly
increases the effectiveness of drug development initiatives. This sophisticated method emphasizes
how crucial molecular docking is to maximize the potential of natural macrocyclic compounds for
contemporary medicinal science [42].
An essential bioinformatics method for examining and understanding ligand–receptor interac-
tions is molecular docking, which provides information on the molecular targets of various ligands.
Through its revolutionary role in chemical separation and medication development from herbal
sources, this method leverages the long history of using herbs as treatments for a wide range of
illnesses. Phytochemicals and drug makers are increasingly concentrating on developing innova-
tive compounds derived mostly from herbs, hence lowering required quantities in the pursuit of
safer and more effective medications. As an example of this paradigm, a plant that is commonly
found in India and is well-known for its use in the treatment of urinary disorders, Rotula aquatica
Lour was thoroughly investigated. After its root was extracted, active chemicals were produced,
and molecular docking analysis was carried out between the 3-O-acetyl-11-keto-β-boswellic acid,
an isolated bioactive molecule, and Tamm–Horsfall protein (THP). This investigation demon-
strates the tactical application of molecular docking techniques in clarifying the medical applica-
tion of natural chemicals derived from herbs, as it found possible interactions that may imply the
prevention of calcium oxalate crystallization [43].
A computational method used in drug development called VS is particularly useful for finding
putative bioactive chemicals. Structure-based VS utilizes two primary methodologies: (i) molecu-
lar docking, which involves docking databases of thousands of compounds onto the active site of
a target of interest to pinpoint top-ranked compounds and (ii) inverse docking, wherein a com-
pound of interest is docked against a variety of targets to unveil potential new targets, as implied
by its name. Figure13.2 shows the molecular docking mechanism and inverse docking mechanism.
The biological significance and structural diversity of natural products highlight the importance
of VS in the field of natural product medication discovery. Though promising, the use of VS in this
area is constrained by the availability of extensive 3D databases of natural products and issues with
interoperability with HTS procedures. To mitigate these obstacles, VS must be strategically inte-
grated with traditional pharmacognosy methodologies. This provides a means of increasing the
effectiveness of natural product-based medication discovery. The integration of VS in the field of
natural product medication discovery is still being researched and developed, and this could
improve the accuracy and effectiveness of recognizing bioactive substances obtained from various
natural sources [44].

292
VS emerges as a pivotal computational technique employed to discern potential bioactive
compounds. This technique speeds up the identification of lead compounds for later drug develop-
ment by methodically analyzing large chemical databases to predict the interactions between
compounds and certain biological targets. Molecular docking, ligand-based screening, and phar-
macophore modeling represent prevalent techniques in VS, proving successful in recognizing mac-
romolecular targets for natural products. The interest in natural product-oriented drug development
initiatives has increased as a result of this success [45]. Moreover, cheminformatics techniques and
molecular modeling techniques work together to develop drugs based on natural products at a
faster rate. These tools not only organize data and interpret results but also facilitate the judicious
filtering of expansive chemical databases before experimental screening, contributing to the effi-
ciency and success of the drug discovery processes [45, 46].
VS techniques, notably molecular docking and shape-matching methodologies serve as predic-
tive tools for illuminating ligand–receptor interactions. These computational methods play a piv-
otal role in expeditiously identifying potential bioactive molecules within vast compound libraries,
significantly curtailing both time and financial expenditures. Particularly, cheminformatics
advancements have made it easier to compare databases of natural products with modern small-
molecule libraries, which has led to the successful identification of macromolecular targets for
natural products. The integration of in silico approaches has sparked a resurgence of interest in
drug discovery, offering a nuanced and efficient pathway that contributes substantive insights to
the ongoing evolution of the field [47].
Databases of compounds
Target of interest
Top ranked compound
Molecular docking Inverse docking
Top ranked target
Database of protein
Compound of interest
Figure 13.2 Two approaches used by virtual screening and molecular docking.
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